Detecting subject motion in medical imaging

ABSTRACT

Presented are concepts for detecting subject motion in medical imaging of a subject. One such concept obtains a motion classification model representing relationships between motion of image features and subject motion values. For each of a plurality of medical slice images of an imaged volume of the subject, an image feature of the medical slice image is extracted. Based on the extracted image feature for each of the plurality of medical slice images, motion information for the image feature is determined. Based on the motion information for the image feature and the obtained motion classification model, a subject motion value is determined.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a U.S. national phase application of InternationalApplication No. PCT/EP2019/068749 filed on Jul. 11, 2019, which claimsthe benefit of U.S. Provisional Application Ser. No. 62/697,655 filedJul. 13, 2018 and is incorporated herein by reference.

FIELD OF THE INVENTION

This invention relates generally to medical imaging of a subject, suchas a person or patient, and more particularly to detecting subjectmotion in a medical imaging of a subject.

BACKGROUND OF THE INVENTION

Subject motion during medical imaging typically causes blurring and/orartifacts in medical images that degrades image quality. Such imagedegradation can result in a need to repeat scans, thus resulting indecreased efficiency and increased costs.

For example, after analysing a random week of Magnetic Resonance Imaging(MRI) activity in a hospital, researchers from the University ofWashington in Seattle found that subject motion can be costly in termsof repeated sequences, which occurred in approximately 20% of MRI exams.Based on such a rate require repetition, a hospital or MRI facility mayover $140,000 in annual revenue per MRI scanner.

Skilled professionals tend to be able to tolerate blurring or artifactsin a medical image if it is not too severe. However, image blurring andartifacts can still result in misdiagnosis. Attempts have therefore beenmade to address such issues by detecting motion artifacts and applyingmotion correction algorithms. However, the effectiveness of suchapproaches is limited.

SUMMARY OF THE INVENTION

The invention aims to at least partly fulfil the aforementioned needs.To this end, the invention provides systems and methods as defined inthe independent claims. The dependent claims provide advantageousembodiments.

There is provided a method for detecting subject motion in medicalimaging of a subject, the method comprising: obtaining a motionclassification model representing relationships between motion of imagefeatures and subject motion values; for each of a plurality of medicalslice images of an imaged volume of the subject, extracting an imagefeature of the medical slice image; based on the extracted image featurefor each of the plurality of medical slice images, determining motioninformation for the image feature, the motion information representingmotion of the image feature in the imaged volume of the subject; anddetermining a subject motion value based on the motion information forthe image feature and the obtained motion classification model.

Proposed is a concept for detecting the presence or absence of subjectmotion in medical imaging of the subject. By extracting image featuresfrom a plurality of medical slice images (which together make up animaged volume, such as MRI DICOM volume for example), motion of theextracted image features in the imaged volume may be determined. Thismotion may then be analysed to determine if there is motion of thesubject. Furthermore, an amount of subject motion may then be identifiedand classified into one of plurality of values or classifications.

Embodiments may therefore help to avoid or reduce a potential formisdiagnosis of a subject. Embodiments may also help to improved (i.e.increase) a radiologist's throughput by avoiding the assignment ofsevere motion cases to radiologists for interpretation/assessment.

Proposed embodiments may also be useful for identifying cases ofinvoluntary subject motion during a medical imaging process.Preventative or corrective action may then be taken during the imagingprocess so as to avoid a need for repeat appointments.

Embodiments may be based on a proposal to employ one or more motionclassification models which represent relationships between motion ofmedical image features and subject motion values. Such models may bebuilt using conventional machine learning and/or image processingtechniques, thus leveraging historical data and/or established knowledgeto improve the accuracy of determinations provided by proposedembodiments.

Improved (e.g. more accurate) medical images and medical imaging-baseddiagnosis of a subject may therefore be facilitated by proposedembodiments. Embodiments may also be employed to improve the efficiencyof a medical imaging facility, thereby providing cost savings andreduced waiting times for appointments.

Proposed embodiments may therefore be of particular relevance toComputerised Tomography (CT) scanning, Positron Emission Tomography(PET)/CT scanning and/or MRI scanning and subject diagnosis since, forexample, it may help to avoid or reduce blurring or artifacts in CTimages, PET/CT images and/or MRI images by detecting subject motion andfacilitating preventative or corrective actions to be undertaken.Proposed concepts may also facilitate accurate assessment or diagnosisof the health of a subject using medical scanning (such as CT scanning,PET scanning or MRI scanning for example). Accordingly, an image featuremay comprise a MRI feature, and a medical slice image may comprise a MRIslice image of an MRI volume. Similarly, an image feature may comprise aCT image feature, and a medical slice image may comprise a CT sliceimage of a CT scan volume.

In some proposed embodiments, a motion classification model may beobtained by generating the motion classification model. For instance,embodiments may generate a motion classification based on historicaldata relating to previously determined subject motion values for imagefeatures extracted from medical slice images. Conventional machinelearning, deep learning and/or image processing techniques may thus beused to build, train and test a motion classification model. Trainingdata and cross validation learning schemes may be employed to refinesuch motion classification models, thereby improving an accuracy ofdeterminations made by embodiments.

For example, in some embodiments, the motion classification model may berefined using a machine learning algorithm with training data relatingto subject motion values for image features of medical slice images.This may provide the advantage of improving the accuracy of motionclassification model.

In some embodiments, the step of extracting an image feature of amedical slice image may comprise: separating the medical slice imageinto a foreground and a background; and extracting a foreground imagefeature from the foreground. Further, extracting a foreground imagefeature may comprise identifying an image feature from at least one of:a spatial domain representation of the foreground; a wavelet domainrepresentation of the foreground; and a spectral domain representationof the foreground. The identified image feature may then be extracted asthe foreground image feature.

Additionally, or alternatively, the step extracting an image feature ofa medical slice image may comprise: segregating the medical slice imageinto a foreground and a background; and extracting a background imagefeature from the background. Further, extracting a background imagefeature may comprise: applying radon transformations to the backgroundto generate a plurality of radon transform profiles; and identifying afeature based on the radon transform profiles. The identified featuremay then be extracted as the background image feature.

Accordingly, in some embodiments, motion information may be obtainedfrom both foreground and background images. By way of example, toaccomplish foreground and background segregation, embodiments may employconventional region-based active contour methods (such as Chan T F, VeseL. Active contours without edges. IEEE Trans Med Imaging. 2001;10(2):266-77). Also, foreground and background segregation may beapplied on individual medical slice images that together form an imagedvolume. In this way, proposed embodiments may be used to detect (andpotentially distinguish between) subject motion in foreground andbackground images.

Some embodiments may further comprise the step of, based on the motioninformation for the image feature, identifying a medical slice image inwhich motion of the extracted image feature exceeds a threshold value.Simple comparison methods may therefore be employed to identify slices(e.g. individual image planes) of an imaged volume which include severesubject motion for example. Straight-forward and reduced-complexityimplementations that facilitate accurate identification of the locationof movement within an imaged volume may thus be realised.

Further, By way of example, embodiments may include determining thethreshold value or model based on historical data relating to previouslydetermined motion of the image feature. Taking account of previouslyobtained or determined information may improve accuracy ofdeterminations or assessments, for example, via comparison and/orrefinement of the threshold value or model based on the historical data.

In some embodiments, the motion classification model may representrelationships between motion of image features and subject motion valuesfor a plurality of different classification methods. The step ofdetermining a subject motion value may then comprise: selectingrelationships between motion of image features and subject motion valuesfor one of the plurality of different classification methods based onthe extracted image feature; and determining a subject motion valuebased on the motion information for the image feature and the selectedrelationships between motion of image features and subject motionvalues. In this way, various classification methods may be employed, andthen a preferred (e.g. most appropriate) classification method may beselected according to an extracted image feature. Since, performance oraccuracy of a classification method may depend on a selected imagefeature, this approach caters for various different image features beingselected whilst also enabling an optimum or preferred classificationmethod to be used. Improved (e.g. more accurate) results may thus beprovided for a range of extracted features.

Embodiments may further comprise the step of generating an output signalbased on the determined subject motion value. Embodiments may be adaptedto provide such an output signal to at least one of: the subject; amedical practitioner; medical imaging apparatus operator; and aradiographer. The output signal may thus be provided to a user ormedical imaging apparatus for the purpose of indicating the presenceand/or severity of subject motion for example.

Some embodiments may further comprise the step of generating a controlsignal for modifying a graphical element based on the subject motionvalue. The graphical element may then be displayed in accordance withthe control signal. In this way, a user (such as a radiologist) may havean appropriately arranged display system that can receive and displayinformation about detected motion of the subject, and that user may beremotely located from the subject. Embodiments may therefore enable auser to remotely detect and monitor motion of a subject (e.g. patient).

According to yet another aspect of the invention, there is providedcomputer program product for detecting subject motion in medical imagingof a subject, wherein the computer program product comprises acomputer-readable storage medium having computer-readable program codeembodied therewith, the computer-readable program code configured toperform all of the steps of an embodiment.

A computer system may be provided which comprises: a computer programproduct according to an embodiment; and one or more processors adaptedto perform a method according to an embodiment by execution of thecomputer-readable program code of said computer program product.

In a further aspect, the invention relates to a computer-readablenon-transitory storage medium comprising instructions which, whenexecuted by a processing device, execute the steps of the method ofdetecting subject motion in medical imaging of a subject according to anembodiment.

According to another aspect of the invention, there is provided a systemfor detecting subject motion in medical imaging of a subject, the systemcomprising: an interface component adapted to obtain a motionclassification model representing relationships between motion of imagefeatures and subject motion values; a feature extraction componentadapted, for each of a plurality of medical slice images of an imagedvolume of the subject, extract an image feature of the medical sliceimage; a data processing component adapted to determine motioninformation for the image feature based on the extracted image featurefor each of the plurality of medical slice images, the motioninformation representing motion of the image feature in the imagedvolume of the subject; and a motion determination component adapted todetermine a subject motion value based on the motion information for theimage feature and the obtained motion classification model.

It will be appreciated that all or part of a proposed system maycomprise one or more data processors. For example, the system may beimplemented using a single processor which is adapted to undertake dataprocessing in order to determine subject motion.

The system for detecting subject motion in medical imaging of a subjectmay be remotely located from the medical imaging apparatus, and medicalimage data may be communicated to the system unit via a communicationlink.

The system may comprise: a server device comprising the interfacecomponent, feature extraction component and data processing component;and a client device comprising the motion determination component.Dedicated data processing means may therefore be employed for thepurpose of determining motion information, thus reducing processingrequirements or capabilities of other components or devices of thesystem.

The system may further comprise a client device, wherein the clientdevice comprises the interface component, feature extraction component,data processing component and motion determination component. In otherwords, a user (such as a radiologist) may have an appropriately arrangedclient device (such as a laptop, tablet computer, mobile phone, PDA,etc.) which processes received medical image data (e.g. medical sliceimages) in order to determine a subject motion value.

Thus, processing may be hosted at a different location from where themedical imaging happens. For example, for reasons of computingefficiency it might be advantageous to execute only part of theprocessing at the medical imaging location, thereby reducing associatedcosts, processing power, transmission requirements, etc.

Thus, it will be understood that processing capabilities may thereforebe distributed throughout the system in different ways according topredetermined constraints and/or availability of processing resources.

Embodiments may also enable some of the processing load to bedistributed throughout the system. For example, pre-processing may beundertaken at a medical imaging system. Alternatively, or additionally,processing could be undertaken at a communication gateway. In someembodiments, processing may be undertaken at a remote gateway or sever,thus relinquishing processing requirements from an end-user or outputdevice. Such distribution of processing and/or hardware may allow forimproved maintenance abilities (e.g. by centralising complex orexpensive hardware in a preferred location). It may also enablecomputational load and/or traffic to be designed or located within anetworked system according to the processing capabilities available. Apreferable approach may be to process medical image data locally andtransmit extracted data for full processing at a remote server.

Embodiments may be implemented in conjunction with pre-existing,pre-installed or otherwise separately-provisioned medical imagingapparatus (such as a CT scanner, PET scanner or MRI scanner), andsignals, data or images from such apparatus may be received andprocessed in accordance with proposed concepts. Other embodiments may beprovided with (e.g. integrated into) medical imaging apparatus (such asCT scanning apparatus or an MRI scanner).

These and other aspects of the invention will be apparent from andelucidated with reference to the embodiment(s) described hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

Examples in accordance with aspects of the invention will now bedescribed in detail with reference to the accompanying drawings, inwhich:

FIG. 1 is a simplified block diagram of a system for detecting subjectmotion in a MRI scan of a subject according to an embodiment;

FIG. 2 is a flow diagram of an exemplary foreground and backgroundsegregation algorithm that may be employed by an embodiment;

FIG. 3 is a flow diagram of a method for extracting image features fromforeground images that may be employed by an embodiment;

FIG. 4A illustrates an example of an MRI image wherein only foregroundinformation of the image has been maintained (and wherein the backgroundinformation has been removed);

FIG. 4B illustrates an example of a CT image wherein only foregroundinformation of the image has been maintained (and wherein the backgroundinformation has been removed);

FIG. 5A depicts a distribution plot in the spatial domain for motion andnon-motion cases, wherein the distribution plot for the non-motion caseis depicted using a line indicated with circles, and wherein the plotfor the motion case is depicted using a line indicated with triangles;

FIG. 5B depicts a distribution plot in the spectral domain for motionand non-motion cases, wherein the distribution plot for the non-motioncase is depicted using a line indicated with circles, and wherein theplot for the motion case is depicted using a line indicated withtriangles;

FIG. 6 depicts exemplary wavelet decomposition images of motion andnon-motion cases;

FIG. 7 is a flow diagram of an exemplary method for extracting imagefeatures from background images that may be employed by an embodiment;

FIG. 8 shows an example of how motion ripples may be visible inbackground images of an MRI slice image;

FIG. 9 illustrates an example of motion ripples may be visualised usingforeground and background segregation;

FIG. 10A is an exemplary plot of background image entropy for each ofeighteen images for cases with motion and no motion, wherein the valuesof background image entropy for the case with motion are depicted usinga line indicated with circles, and wherein values of background imageentropy for the case with no motion are depicted using a line indicatedwith triangles;

FIG. 10B is an exemplary plot of radon transform value (applied at 45degrees) for each of eighteen images for cases with motion and nomotion, wherein the values of the radon transform for the case withmotion are depicted using a line indicated with circles, and wherein thevalues of the radon transform for the case with no motion are depictedusing a line indicated with triangles;

FIG. 11 depicts a graph plotting a peak motion value of backgroundfeature for each of a plurality of medical slice images, wherein thegraph includes a bold line representing threshold value that may be usedto distinguish between severe and non-severe motion for a medical sliceimage;

FIG. 12 is a simplified block diagram of a system detecting subjectmotion in medical imaging of a subject according to another embodiment;and

FIG. 13 is a simplified block diagram of a computer within which one ormore parts of an embodiment may be employed.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Proposed is a concept for detecting the presence or absence of subjectmotion during a medical scan. This may enable the identification ofsubject motion in a given imaging volume (such as a MRI DICOM volume)that is formed from medical image slices. It may, for example,facilitate the identification of medical image slices that includedifferent grades or levels of motion artifacts.

To enable the detection of subject motion, image features may beextracted from medical images and then motion information representativeof motion of the extracted image features within the imaged volume maybe determined. The determined motion information may then be analysed inconjunction with a motion classification model representingrelationships between motion of image features and subject motionvalues, thereby enabling the determination of a subject motion value orclassification for the extracted feature(s).

Use of a motion classification model may enable movement of extractedimage features to be quantified or classified based on established orlearnt information about motion of features in medical images/volumesand subject motion. Such models may be developed based on traditionalimage processing and machine-learning techniques for improved accuracy.

Embodiments may, for example, be useful for improving medical scanningand assessment of subjects. Such subjects may, for instance, include adisabled person, an elderly person, an injured person, a medicalpatient, etc. Elderly persons can mean persons above 50 years, above 65years, above 70, or above 80 years old, for example.

Illustrative embodiments may be utilized in many different types ofmedical imaging apparatus and/or medical imaging facilities, such as ahospital, ward, research facility, etc.

By way of example, subject motion detection may be useful whileallocating or scheduling cases for radiologists. Using a proposedembodiment, a user may, for example, filter all the severe subjectmotion cases to increase the throughput of radiologists.

Also, embodiments may be integrated in medical imaging systems toprovide real-time feedback to technicians regarding detected subjectmotion (e.g. involuntary patient motion) while scanning is in progress.Using such feedback, a technician may check the severity of the motionand, if necessary, abandon and restart the scanning while the subject isstill on the scanner table. In this way, a subject need not re-visit themedical imaging facility for a repeat scan.

Proposed embodiments may identify mild/minor subject motion in medicalimages. The motion artifacts may then be corrected using suitable motioncorrection algorithms.

In order to provide a context for the description of elements andfunctionality of the illustrative embodiments, the Figures are providedhereafter as examples of how aspects of the illustrative embodiments maybe implemented. It should therefore be appreciated the Figures are onlyexamples and are not intended to assert or imply any limitation withregard to the environments, systems or methods in which aspects orembodiments of the present invention may be implemented.

Embodiments of the present invention are directed toward enabling motionof a subject in a medical scan to be detected and potentiallyclassified. This be useful for improving scanning accuracy orefficiency, e.g. by avoiding or reducing a number of medical scans thata ruined by subject motion.

Embodiments may employ conventional machine-learning and imageprocessing techniques to build subject motion classification models foridentifying or classifying a severity of the motion based on movement ofimage features in medical images. Based on training data (e.g.historical data. previously-established results and/or previousobservations), feature selection and cross-validation learning schemesmay be employed to generate the classification models. Such models maythen also be tested (e.g. using test data) by selecting and extractingimage features from medical images and using the models to determine(e.g. classify) severity of subject motion (which can then becheck/compared against established or correct results).

By determining a measure or classification of subject motion from one ormore features of medical slice images, embodiments may enable theidentification of subject motion that is significant and/orrepresentative of motion that cannot be catered for (e.g. corrected,ignored or read through). This may help to reduce a number of wastedmedical scans and provide improved medical images. Thus, embodiments maybe useful for real-time medical scan monitoring purposes, for example toassess if a subject is moving excessively during a medical scanningprocess.

Subject motion may be detected or classified from medical slice imagesproduced by medical imaging devices and systems that already exist.Accordingly, the proposed concepts may be used in conjunction withexisting medical imaging systems/methods (such as those employed for CT,PET/CT and/or MRI scanning for example). Because many such medicalimaging methods/systems are known and any one or more of these may beemployed, detailed description of such methods/systems is omitted fromthis description.

FIG. 1 shows an embodiment of a system 100 for detecting subject motionin a MRI scan of a subject according to an embodiment.

The system 100 comprises an interface component 110 adapted to obtain amotion classification model 10 representing relationships between motionof MRI features and subject motion values. Here, the interface component110 is adapted to generate a motion classification model based onhistorical data 115 (e.g. stored in a database) relating to previouslydetermined subject motion values for MRI features extracted from MRIslice images.

The historical data 115 is communicated to the interface component 110via a wired or wireless connection. By way of example, the wirelessconnection may comprise a short-to-medium-range communication link. Forthe avoidance of doubt, short-to-medium-range communication link may betaken to mean a short-range or medium-range communication link having arange of up to around one hundred (100) meters. In short-rangecommunication links designed for very short communication distances,signals typically travel from a few centimetres to several meters,whereas, in medium-range communication links designed for short tomedium communication distances, signals typically travel up to onehundred (10)0 meters. Examples of short-range wireless communicationlinks are ANT+, Bluetooth, Bluetooth low energy, IEEE 802.15.4, ISA100a,Infrared (IrDA), Near Field Communication (NFC), RFID, 6LoWPAN, UWB,Wireless HART, Wireless HD, Wireless USB, ZigBee. Examples ofmedium-range communication links include Wi-Fi, ISM Band, Z-Wave. Here,the output signals are not encrypted for communication via the wired orwireless connection in a secured manner. However, it will be appreciatedthat, in other embodiment, one or more encryption techniques and/or oneor more secure communication links may be employed for the communicationof signals/data in the system.

Furthermore, in the example of FIG. 1, the interface component isadapted to refine the motion classification model using amachine-learning algorithm with training data relating to subject motionvalues for MRI features of MRI slice images.

The system 100 also comprises a feature extraction component 120adapted, for each of a plurality of MRI slice images of an MRI volume ofthe subject, to extract an MRI feature of the MRI slice image. Morespecifically, in this example, the feature extraction component 120 isadapted to separate the MRI slice image into a foreground and abackground, and to then extract at least one of: a foreground MRIfeature from the foreground; and a background MRI feature from thebackground

By way of example, to extract a foreground MRI feature, the featureextraction component 120 is adapted to identify a feature from at leastone of: a spatial domain representation of the foreground; a waveletdomain representation of the foreground; and a spectral domainrepresentation of the foreground. The identified features is thenextracting as the foreground MRI feature.

By way of further example, to extract a background MRI feature, thefeature extraction component 120 is adapted to apply radontransformations to the background so as to generate a plurality of radontransform profiles. The feature extraction component 120 is then adaptedto identify a feature based on the radon transform profiles, andextracts the identified feature as the background MRI feature.

The system 100 further comprises a data processing component 122 that isadapted to determine motion information for the MRI feature based on theextracted MRI feature for each of the plurality of MRI slice images.More specifically, the motion information determined by the dataprocessing component 122 represents motion of the MRI feature in the MRIvolume of the subject.

For this purpose, the data processing component 122 of the system 100may communicate with one or more data processing resources available inthe internet or “cloud” 50. Such data processing resources may undertakepart or all of the processing required to determine motion informationfor an extracted MRI feature.

The determined motion information is provided to a motion determinationcomponent 124 of the system 110. The motion determination component 124is adapted to determine a subject motion value based on the motioninformation for the MRI feature and the obtained motion classificationmodel. Again, for this purpose, the motion determination component 124may communicate with one or more data processing resources available inthe internet or “cloud” 50. Such data processing resources may undertakepart or all of the processing required to determine a subject motionvalue.

Thus, it will be appreciated that the embodiment may employ distributedprocessing principles.

The data processing system 110 is further adapted to generate an outputsignal 130 representative of a determined subject motion value. In otherwords, after determining a subject motion value based on motioninformation for the MRI feature and the obtained motion classificationmodel (either with or without communicating with data processingresources via the internet or “cloud”), an output signal 130representative of or determined subject motion value is generated.

The system further comprises a graphical user interface (GUI) 160 forproviding information to one or more users. The output signal 130 isprovided to the GUI 160 via wired or wireless connection. By way ofexample, the wireless connection may comprise a short-to-medium-rangecommunication link. As indicated in FIG. 1, the output signal 130 isprovided to the GUI 160 from the data processing unit 110. However,where the system, has made use of data processing resources via theinternet or cloud 50), an output signal may be made available to the GUI160 via the internet or cloud 50.

Based on the output signal 130, the GUI 160 is adapted to communicateinformation by displaying one or more graphical elements in a displayarea of the GUI 160. In this way, the system may communicate informationabout a subject motion value that may be useful for indicating if thesubject has moved excessively during a MRI scanning process. Forexample, the GUI 160 may be used to display graphical elements to amedical practitioner, a radiologist, a MRI apparatus operator, MRItechnician or the like. Alternatively, or in addition, the GUI 160 maybe adapted to display graphical elements to the subject.

From the above description of the embodiments of FIG. 1, it will beunderstood that there is proposed a system for identifying subjectmotion in a MRI scan of the subject. The scan is assumed to be of aimaged volume (e.g. 3D segment or part) of the subject and formed from aplurality of parallel MRI slice images that are offset from each otheralong a central axis of the volume (with each MRI slice image being in aplane perpendicular to the central axis).

Accordingly, based on the motion information for an extracted MRIfeature, an MRI slice image in which motion of the extracted MRI featureexceeds a threshold value may be identified. In this way, MRI sliceimages in which feature motion is excessive (e.g. exceeds an acceptableamount) may be identified. The location(s) of excessive motion in thescanned volume may therefore be identified, further helping to establishif and how the subject motion may have occurred.

By way of further example, the threshold value may be determined (e.g.calculated) based on historical data relating to previously determinedmotion of the MRI feature. In this way, a threshold value fordistinguishing excessive subject motion from acceptable (e.g.correctable or fixable) subject motion may be refined or improved forimproved accuracy or usability.

Although the example embodiment of FIG. 1 detailed above is described inrelation to MRI scanning, it will be appreciated that proposed conceptsmay be extended to other medical scanning modalities such as CTscanning, PET scanning and the like.

Also, from the above description, it will be understood that a proposedmethod according to an embodiment may comprise the following mainstages:

(i) Model Acquisition—obtaining a motion classification modelrepresenting relationships between motion of image features and subjectmotion values;

(ii) Feature Extraction—feature extraction from medical images tocapture motion information caused due to patient motion;

(iii) Volume-Level Feature Determination—for deriving motion informationat volume level, since feature extraction is at slice level; and

(iv) Motion Classification—classification of the motion information foridentifying an amount or level of subject motion.

Exemplary approaches for these main stages will now be detailed below.

Model Acquisition

Embodiments may employ traditional machine-learning, deep learningand/or image processing techniques to build motion classification modelswhich link image feature motion to various levels or classification ofsubject motion. Thus, such models may be derived from historicalinformation relating to previous test result and/or observations, andthe models may be trained and tested using various forms of input data.

Based on the training data, feature selection and cross-validation oflearning schemes may be employed to generate classification models.

Feature Extraction

From medical images, features may be extracted from both foreground andbackground images.

By way of example, to accomplish foreground and background segregation,embodiments may employ Chan and Vese's region-based active contourmethod (Chan T F, Vese L. Active contours without edges. IEEE Trans MedImaging. 2001; 10(2):266-77) with 350 iterations and defining theinitial mask as equivalent to 1 pixel size less than original imagesize.

Foreground and background segregation may be applied on individualslices in a volume.

By way of example, a flow diagram of a foreground and backgroundsegregation algorithm 20 that may be employed is depicted in FIG. 2.

In FIG. 2, steps 210 through 260 comprise loading of imaging volume(step 220) to separate the foreground and background for each slice(steps 250 and 260). In step 230, it is checked if all medical sliceimages have been subjected to the segregation. If they have, the processends in step 240. After segregation of steps 250 and 260, the medicalslice image is re-sized in step 270 and the mask is initialized in step280 in both the horizontal and vertical direction. A Chan and Vese'ssegmentation method is applied in step 300 with the pre-defined numberof iterations identified in step 290 and the segmented image issubjected to filling (step 310) and dilation (step 320) operations andsubsequently resized to original size (step 330) to obtain theforeground (step 340) and background (step 350) separated image.

Foreground Feature Extraction

Referring now to FIG. 3, there is depicted a flow diagram of a method 30for extracting image features from foreground images. Such a featureextraction technique may thus be used for capturing motion informationcaused due to subject motion in foreground images.

In FIG. 3, the steps 360 through 410 comprise loading of imaging volume(step 370) to extract the foreground and background features for eachmedical image slice of the imaging volume (steps 400 and 410). In step380, it is checked if all medical slice images have been subjected tothe feature extraction. If they have, the process ends in step 390.

First for each medical slice image of the imaging volume, foreground andbackground images are separated (step 420). Feature are extracted fromthe foreground image (step 430), spatial domain representation (step440), spectral domain representation (step 450) and wavelet domainrepresentation (step 460).

FIG. 3 thus depicts a method (30) for feature extraction in which aforeground and background segregation algorithm is employed to keep onlyforeground information of the image by discarding the information thatexists in background of the image.

An example of an MRI image wherein only foreground information of theimage has been maintained is depicted in FIG. 4A Further, an example ofa CT image wherein only foreground information of the image has beenmaintained is depicted in FIG. 4B.

On the foreground image, image features may be extracted from a spatial,spectral and/or wavelet domain representation of the image. For example:

Spatial Features: As a part of spatial feature set, on foregroundimages, spatial domain features such as image resolution features,histogram based features, autocorrelation features and focus featuresmay be extracted.

Spectral features: As a part of spectral feature set, on foregroundimages, frequency spectrum features such as spectral histogram andspectral energy based features may be extracted.

By way of example, FIG. 5A depicts a distribution plot in the spatialdomain for motion and non-motion cases. The distribution plot for thenon-motion case is depicted using a line indicated with circles, whereasthe plot for the motion case is depicted using a line indicated withtriangles. From FIG. 5A it can be seen that the distribution plot in thespatial domain for the non-motion case is narrower and has a higher peakvalue than that of the motion case.

By way of further example, FIG. 5B depicts a distribution plot in thespectral domain for motion and non-motion cases. The distribution plotfor the non-motion case is depicted using a line indicated with circles,whereas the plot for the motion case is depicted using a line indicatedwith triangles. From FIG. 5B it can be seen that the distribution plotin the spectral domain for the non-motion case is narrower, shifted leftand has a higher peak value than that of the motion case.

Wavelet features: As a part of wavelet feature set, on foregroundimages, wavelet decomposition, and wavelet packed decomposition featuresmay be extracted. By way of example, FIG. 6 depicts the waveletdecomposition images of motion and non-motion cases. In FIG. 6, thefirst (i.e. top) to third (i.e. bottom) row of images correspond toapproximation and detailed coefficients for no motion, moderate motionand severe motion examples, respectively.

Background Feature Extraction

Inspired by the natural wave formed ripples, embodiment may employfeature extraction techniques to capture motion ripples caused bysubject motion in background images. For example, referring to FIG. 7,there is depicted a flow diagram of a method for extracting imagefeatures from background images.

In FIG. 7, the steps 700 through 740 comprise loading of imaging volume(step 710) to extract the background features of each slice (steps 730and 740). In step 720, it is checked if all medical slice images havebeen subjected to the feature extraction process. If they have, theprocess ends in step 725. First for each medical slice image of theimaged volume, foreground and background images are separated (step750). The separated background image is obtained (step 760) and a radontransformation then applied at different orientations (step 770) togenerate various statistical measures (step 780) that act as backgroundfeatures for classifier models.

Referring now to FIG. 8, there is shown an example of how motion ripplesmay be visible in background images of an MRI slice image.

Using foreground and background segregation (as exemplified by themethod of FIG. 2), background images with motion ripples were capturedas shown in FIG. 9. Here, on the background image, a radon transform wasapplied at 1, 45, 90 and 135 degrees to get 1-Dimensional profiles.Thirty-eight features based on the peaks, envelope and rms values werethen extracted from radon transform profiles.

Sample background feature value plots are shown in FIG. 10 for motionand non-motion images.

More specifically, FIG. 10A depicts plots of background image entropyfor each of eighteen images for cases with motion and no motion. Thevalues of background image entropy for the case with motion are depictedusing a line indicated with circles, whereas values of background imageentropy for the case with no motion are depicted using a line indicatedwith triangles. From FIG. 10A it can be seen that values of backgroundimage entropy for the case with motion are higher and exhibit a highervariance between images than the case with no motion.

Also, FIG. 10B depicts plots of radon transform value (applied at 45degrees) for each of 18 images for cases with motion and no motion. Thevalues of the radon transform for the case with motion are depictedusing a line indicated with circles, whereas values of the radontransform for the case with no motion are depicted using a lineindicated with triangles. From FIG. 10B it can be seen that the radontransform values for the case with motion are higher and exhibit ahigher variance between images than the case with no motion.

Volume-Level Feature Determination

As explained above, foreground and background features may be extractedfrom medical slice images (i.e. at slice level). To derive motioninformation at a ‘volume level’ (i.e. information regarding featuremovement in the scanned volume), it is proposed to translate theextracted features to an imaged volume level. Such features derived atimaged volume level may therefore be based on the foreground andbackground features extracted at slice level.

Motion information is typically required at imaged volume levels so asto enable segregation of the imaged volumes based on thelevels/classification of motion (e.g. no motion, mild motion, moderatemotion and severe motion). It is therefore proposed to aggregateslice-level feature information to a volume-level representation. Forthis, standard statistical parameters may be employed, thus facilitatinga volume level representation to be obtained from the individualslice-level features.

Exemplary statistical parameters that may be used include: (i) Minimum,(ii) Mean, (iii) Maximum, (iv) Standard deviation, (v) Kurtosis, (vi)Ratio of maximum value to mean value, and (vii) Ratio of mean value tominimum value.

In addition, volume-level features are proposed which combine an energylevel of slices and the extracted feature vectors at slice level. Thiscombination may identify the feature vectors of the medical slice imagesthat change predominately due to the motion rather than the anatomy.

By way of example, let, E(n) and FV(n) represent energy and featurevalues of individual medical slice images, respectively. Also, let DE(n)and DF(n) respectively be the percentage of difference in energy levelsand feature values of successive medical slice images with respect tothe previous medical slice image. From these values, the followingparameters may be extracted, wherein α_(n) is the learning parameter andis identified experimentally by varying the values provided in α₁ and α₂to obtain the best performance:

Difference of Energy & Feature Values:

1. Find “N” such that; N:

DE(n) ≤ α₁&  DF(n) ≥ α₂

2. Find N such that; N:

$\frac{{DF}(n)}{{DE}(n)} \geq {\alpha\mspace{14mu}({or})\mspace{14mu}\frac{{DF}(n)}{{DE}(n)}} \leq \frac{1}{\alpha}$

Overall Change in Energy & Feature Values:

3. Find N such that; N:

${{{{FV}(n)} \geq {\alpha_{1}\frac{\sum\limits_{i}^{L}{{FV}(n)}}{L}}}\&}1\mspace{11mu}\alpha_{2}\frac{\sum\limits_{i}^{L}{E(n)}}{L}$

Descriptive Statistics of Energy & Feature Values:

4. Descriptive statics of

$\frac{D{F(n)}}{D{E(n)}},$where α₁=α₂=[0.5, 1, 1.5, 2, 3, 4] & α=[1, 1.5, 2, 3, 4]

Motion Classification

The inventors have explored the use of several conventionalclassification methods for identifying an imaged volume as motion orno-motion. Such methods used with the default parameters are shown inTable 1 below.

TABLE 1 Classifier Type Parameters Decision Tree N_Tree = No. Features −1 N_Leaf = 1 KNN k = sqrt(No. Features) Euclidean Distance RandomForests No_F = sqrt(No. Features) Neural Networks Support Vector Machine(Linear) Support Vector Machine Sigma = 1 (Gaussian) Ensemble (BaggedTrees) 63.2% of samples with replacement N_Weak_Leamers = 30 Ensemble(Boosted Trees) N_Weak_Leamers = 30

Results obtained through such investigations indicate that the SupportVector Machine (Gaussian) classification method provides the bestresults. However, this simply indicates that the Support Vector Machine(Gaussian) may be preferable in some instances (e.g. when the defaultparameters are used). It should therefore be understood that the otherclassification methods may be employed in other embodiments, and mayeven be preferable (e.g. provide more accurate results) in differentcircumstances.

Also, it is noted that, where a motion classification model employs aplurality of different classification methods, a preferred (e.g. moreaccurate) classification method may depend on the extracted feature(s).Accordingly, when determining a subject motion value, embodiments mayinclude the concept of selecting one of a plurality of differentclassification method based on the feature(s) extracted from a medicalslice image.

Background Feature Analysis to Detect Motion Slices

It is noted that, in full body scans, sometimes only a few medical imageslices of the scanned volume might be corrupted with motion artifacts,and the rest of the medical slice images in the scanned volume maytherefore be used for diagnosis purposes. Accordingly, identifying thespecific medical slice image(s) corrupted by motion artifacts may beuseful. For example, it may help to reduce a Field of View (FOV) inrepeat scans. Such a reduction in FOV for repeat scans may increasepatient comfort and/or improve scanning throughput.

By way of example, to identify motion at medical slice image level, itis proposed to use the background image features that capture motionripples as described in above.

Referring to FIG. 11, there is depicted a graph plotting a peak motionvalue of background feature for each of a plurality of medical sliceimages. On the graph, a bold line represents a threshold value (Th) thatmay be used to distinguish between severe and non-severe motion for amedical slice image.

The threshold value may, for example, be learnt from a training set thatcan differentiate severe and non-severe motion at slice level.Additionally, or alternatively, the threshold value may be determinedbased on historical data relating to previously determined motion ofimage features.

From FIG. 11, it is seen that medical slice images having a peak motionvalue exceeding the threshold can be easily identified. In this way,embodiments may be adapted to differentiate severe and non-severe motionin individual medical slice images.

Referring now to FIG. 12, there is depicted another embodiment of asystem according to the invention comprising a CT scanning system 810adapted to scan a volume of a subject and generate a plurality of CTimage slices the scanned volume. Here, the CT scanning system 810comprises a conventional CT scanning system 810 that may, for example,be available for use in a CT scanning/imaging facility.

The CT scanning system 810 communicates output signals representative ofacquired CT image slices via the internet 820 (using a wired or wirelessconnection for example) to a remotely located data processing system 830for detecting subject motion in MRI of a subject (such as server).

The data processing system 830 is adapted to receive the one or moreoutput signals from the CT scanning system 810 (e.g. as CT image slicedata). The system is also adapted to obtain a motion classificationmodel representing relationships between motion of CT image features andsubject motion values (e.g. from a local or remote database and/or via auser input interface).

The data processing system 830 processes the CT scanning system outputsignals and the motion classification model in accordance with a methodaccording to a proposed embodiment to determine a subject motion value.More specifically, the method: extracts an MRI feature of each MRI sliceimage; determines motion information for the MRI feature based on theextracted MRI feature for each of the plurality of MRI slice images, themotion information representing motion of the MRI feature in the MRIvolume of the subject; and then determines a subject motion value basedon the motion information for the MRI feature and the obtained motionclassification model.

The data processing system 830 is further adapted to generate outputsignals representative of a determined subject motion value. Thus, thedata processing 830 provides a centrally accessible processing resourcethat can receive information from MRI system and run one or morealgorithms to detect subject motion in the MRI of the subject.Information relating to the detected subject motion can be stored by thedata processing system (for example, in a database) and provided toother components of the system. Such provision of information about adetected or inferred subject motion may be undertaken in response to areceiving a request (via the internet 820 for example) and/or may beundertaken without request (i.e. ‘pushed’).

For the purpose of receiving information about a detected or inferredsubject motion from the data processing system, and thus to enable thesubject motion to be monitored accurately and/or in context, the systemfurther comprises first 840 and second 850 mobile computing devices.

Here, the first mobile computing device 840 is a mobile telephone device(such as a smartphone) with a display for displaying graphical elementsrepresentative of detected subject motion. The second mobile computingdevice 850 is a mobile computer such as a Laptop or Tablet computer witha display for displaying graphical elements representative of detectedsubject motion during a CT scan.

The data processing system 830 is adapted to communicate output signalsto the first 840 and second 850 mobile computing devices via theinternet 820 (using a wired or wireless connection for example). Asmentioned above, this may be undertaken in response to receiving arequest from the first 840 or second 850 mobile computing devices.

Based on the received output signals, the first 840 and second 850mobile computing devices are adapted to display one or more graphicalelements in a display area provided by their respective display. Forthis purpose, the first 840 and second 850 mobile computing devices eachcomprise a software application for processing, decrypting and/orinterpreting received output signals in order to determine how todisplay graphical elements. Thus, the first 840 and second 850 mobilecomputing devices each comprise a processing arrangement adapted to oneor more values representative of detected subject motion, and togenerate a display control signal for modifying at least one of thesize, shape, position, orientation, pulsation or colour of the graphicalelement based on the detected motion.

The system can therefore communicate information about an inferred ordetected motion of subject in a CT scan to users of the first 840 andsecond 850 mobile computing devices. For example, each of the first 840and second 850 mobile computing devices may be used to display graphicalelements to a medical practitioner, a radiologist or the subject.

Implementations of the system of FIG. 12 may vary between: (i) asituation where the data processing system 830 communicatesdisplay-ready data, which may for example comprise display dataincluding graphical elements (e.g. in JPEG or other image formats) thatare simply displayed to a user of a mobile computing device usingconventional image or webpage display (which can be web based browseretc.); to (ii) a situation where the data processing system 830communicates raw data set information that the receiving mobilecomputing device then processes to detect subject motion, and thendisplays graphical elements based on the detected motion (for example,using local software running on the mobile computing device). Of course,in other implementations, the processing may be shared between the dataprocessing system 830 and a receiving mobile computing device such thatpart of the data generated at data processing system 830 is sent to themobile computing device for further processing by local dedicatedsoftware of the mobile computing device. Embodiments may thereforeemploy server-side processing, client-side processing, or anycombination thereof.

Further, where the data processing system 830 does not ‘push’information (e.g. output signals), but rather communicates informationin response to receiving a request, the user of a device making such arequest may be required to confirm or authenticate their identity and/orsecurity credentials in order for the information to be communicated.

FIG. 13 illustrates an example of a computer 900 within which one ormore parts of an embodiment may be employed. Various operationsdiscussed above may utilize the capabilities of the computer 900. Forexample, one or more parts of a system for detecting subject motion maybe incorporated in any element, module, application, and/or componentdiscussed herein.

The computer 900 includes, but is not limited to, PCs, workstations,laptops, PDAs, palm devices, servers, storages, and the like. Generally,in terms of hardware architecture, the computer 900 may include one ormore processors 910, memory 920, and one or more I/O devices 970 thatare communicatively coupled via a local interface (not shown). The localinterface can be, for example but not limited to, one or more buses orother wired or wireless connections, as is known in the art. The localinterface may have additional elements, such as controllers, buffers(caches), drivers, repeaters, and receivers, to enable communications.Further, the local interface may include address, control, and/or dataconnections to enable appropriate communications among theaforementioned components.

The processor 910 is a hardware device for executing software that canbe stored in the memory 920. The processor 910 can be virtually anycustom made or commercially available processor, a central processingunit (CPU), a digital signal processor (DSP), or an auxiliary processoramong several processors associated with the computer 900, and theprocessor 910 may be a semiconductor based microprocessor (in the formof a microchip) or a microprocessor.

The memory 920 can include any one or combination of volatile memoryelements (e.g., random access memory (RAM), such as dynamic randomaccess memory (DRAM), static random access memory (SRAM), etc.) andnon-volatile memory elements (e.g., ROM, erasable programmable read onlymemory (EPROM), electronically erasable programmable read only memory(EEPROM), programmable read only memory (PROM), tape, compact disc readonly memory (CD-ROM), disk, diskette, cartridge, cassette or the like,etc.). Moreover, the memory 920 may incorporate electronic, magnetic,optical, and/or other types of storage media. Note that the memory 920can have a distributed architecture, where various components aresituated remote from one another, but can be accessed by the processor910.

The software in the memory 920 may include one or more separateprograms, each of which comprises an ordered listing of executableinstructions for implementing logical functions. The software in thememory 920 includes a suitable operating system (0/S) 950, compiler 940,source code 930, and one or more applications 960 in accordance withexemplary embodiments. As illustrated, the application 960 comprisesnumerous functional components for implementing the features andoperations of the exemplary embodiments. The application 960 of thecomputer 900 may represent various applications, computational units,logic, functional units, processes, operations, virtual entities, and/ormodules in accordance with exemplary embodiments, but the application960 is not meant to be a limitation.

The operating system 950 controls the execution of other computerprograms, and provides scheduling, input-output control, file and datamanagement, memory management, and communication control and relatedservices. It is contemplated by the inventors that the application 960for implementing exemplary embodiments may be applicable on allcommercially available operating systems.

Application 960 may be a source program, executable program (objectcode), script, or any other entity comprising a set of instructions tobe performed. When a source program, then the program is usuallytranslated via a compiler (such as the compiler 940), assembler,interpreter, or the like, which may or may not be included within thememory 920, so as to operate properly in connection with the O/S 950.Furthermore, the application 960 can be written as an object orientedprogramming language, which has classes of data and methods, or aprocedure programming language, which has routines, subroutines, and/orfunctions, for example but not limited to, C, C++, C#, Pascal, BASIC,API calls, HTML, XHTML, XML, php. Python, ASP scripts, FORTRAN, COBOL,Perl, Java, ADA, .NET, and the like.

The I/O devices 970 may include input devices such as, for example butnot limited to, a mouse, keyboard, scanner, microphone, camera, etc.Furthermore, the I/O devices 970 may also include output devices, forexample but not limited to a printer, display, etc. Finally, the I/Odevices 970 may further include devices that communicate both inputs andoutputs, for instance but not limited to, a NIC or modulator/demodulator(for accessing remote devices, other files, devices, systems, or anetwork), a radio frequency (RF) or other transceiver, a telephonicinterface, a bridge, a router, etc. The I/O devices 970 also includecomponents for communicating over various networks, such as the Internetor intranet.

If the computer 900 is a PC, workstation, intelligent device or thelike, the software in the memory 920 may further include a basic inputoutput system (BIOS) (omitted for simplicity). The BIOS is a set ofessential software routines that initialize and test hardware atstartup, start the O/S 950, and support the transfer of data among thehardware devices. The BIOS is stored in some type of read-only-memory,such as ROM, PROM, EPROM, EEPROM or the like, so that the BIOS can beexecuted when the computer 900 is activated.

When the computer 900 is in operation, the processor 910 is configuredto execute software stored within the memory 920, to communicate data toand from the memory 920, and to generally control operations of thecomputer 900 pursuant to the software. The application 960 and the O/S950 are read, in whole or in part, by the processor 910, perhapsbuffered within the processor 910, and then executed.

When the application 960 is implemented in software it should be notedthat the application 960 can be stored on virtually any computerreadable medium for use by or in connection with any computer relatedsystem or method. In the context of this document, a computer readablemedium may be an electronic, magnetic, optical, or other physical deviceor means that can contain or store a computer program for use by or inconnection with a computer related system or method.

The application 960 can be embodied in any computer-readable medium foruse by or in connection with an instruction execution system, apparatus,or device, such as a computer-based system, processor-containing system,or other system that can fetch the instructions from the instructionexecution system, apparatus, or device and execute the instructions. Inthe context of this document, a “computer-readable medium” can be anymeans that can store, communicate, propagate, or transport the programfor use by or in connection with the instruction execution system,apparatus, or device. The computer readable medium can be, for examplebut not limited to, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, device, or propagationmedium.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, optimized forembedded implementation, such as the “C” programming language or similarprogramming languages. The computer readable program instructions mayexecute entirely on the user's computer, partly on the user's computer,as a stand-alone software package, partly on the user's computer andpartly on a remote computer or entirely on the remote computer orserver. In the latter scenario, the remote computer may be connected tothe user's computer through any type of network, including a local areanetwork (LAN) or a wide area network (WAN), or the connection may bemade to an external computer (for example, through the Internet using anInternet Service Provider). In some embodiments, electronic circuitryincluding, for example, programmable logic circuitry, field-programmablegate arrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

From the above description, it will be appreciated that embodiments maytherefore be useful for detecting and classifying motion of a subjectduring an MRI scan. Detected subject motion can be used both forreal-time monitoring and alerts, as well as to detect when MRI imagesare/aren't reliable.

The description has been presented for purposes of illustration anddescription, and is not intended to be exhaustive or limited to theinvention in the form disclosed. Many modifications and variations willbe apparent to those of ordinary skill in the art. Embodiments have beenchosen and described in order to best explain principles of proposedembodiments, practical application(s), and to enable others of ordinaryskill in the art to understand that various embodiments with variousmodifications are contemplated.

The invention claimed is:
 1. A method for detecting subject motion inmedical imaging of a subject, the method comprising: obtaining a motionclassification model representing relationships between motion of imagefeatures and previously determined subject motion values for a pluralityof different classification methods; for each medical slice image of aplurality of medical slice images of an imaged volume of the subject,extracting an image feature of the medical slice image; based on theextracted image feature for each medical slice image of the plurality ofmedical slice images, determining motion information for the imagefeature, the motion information representing motion of the image featurein the imaged volume of the subject; selecting relationships between themotion of image features and the previously determined subject motionvalues for a classification method of the plurality of differentclassification methods based on the extracted image feature; anddetermining a subject motion value based on the motion information forthe image feature and the selected relationships between the motion ofimage features and the previously determined subject motion values. 2.The method of claim 1, wherein obtaining the motion classification modelcomprises: generating the motion classification model based onhistorical data relating to the previously determined subject motionvalues for image features extracted from medical slice images.
 3. Themethod of claim 2, wherein obtaining the motion classification modelfurther comprises: refining the motion classification model using amachine learning algorithm with training data relating to subject motionvalues for image features of medical slice images.
 4. The method ofclaim 1, wherein extracting the image feature of the medical slice imagecomprises: separating the medical slice image into a foreground and abackground; and extracting a foreground image feature from theforeground.
 5. The method of claim 4, wherein extracting the foregroundimage feature comprises: identifying an image feature from at least oneof: a spatial domain representation of the foreground, a wavelet domainrepresentation of the foreground, or a spectral domain representation ofthe foreground; and extracting the identified image feature as theforeground image feature.
 6. The method of claim 1, wherein extractingthe image feature of the medical slice image comprises: segregating themedical slice image into a foreground and a background; and extracting abackground image feature from the background.
 7. A method for detectingsubject motion in medical imaging of a subject, the method comprising:obtaining a motion classification model representing relationshipsbetween motion of image features and previously determined subjectmotion values; for each of a plurality of medical slice images of animaged volume of the subject, extracting an image feature of the medicalslice image, wherein extracting the image feature comprises: segregatingthe medical slice image into a foreground and a background; applyingradon transformations to the background to generate a plurality of radontransform profiles; identifying a background image feature based on theradon transform profiles; and extracting the identified background imagefeature as the image feature: based on the extracted image feature foreach of the plurality of medical slice images, determining motioninformation for the image feature, the motion information representingmotion of the image feature in the imaged volume of the subject; anddetermining a subject motion value based on the motion information forthe image feature and the obtained motion classification model.
 8. Themethod of claim 1, further comprising: based on the motion informationfor the image feature, identifying a medical slice image in which motionof the extracted image feature exceeds a threshold value.
 9. The methodof claim 8, further comprising: determining the threshold value based onhistorical data relating to previously determined motion of the imagefeature.
 10. A non-transitory computer readable medium storing computerprogram code instructions for detecting subject motion in medicalimaging of a subject, which when executed by at least one processor,cause the at least one processor to implement the method of claim
 1. 11.A system for detecting subject motion in medical imaging of a subject,the system comprising: an interface configured to obtain a motionclassification model representing relationships between motion of imagefeatures and previously determined subject motion values for a pluralityof different classification methods; and at least one processorconfigured to: extract an image feature of each medical slice image of aplurality of medical slice images of an imaged volume of the subject;determine motion information for the image feature based on theextracted image feature of each medical slice image of the plurality ofmedical slice images, the motion information representing motion of theimage feature in the imaged volume of the subject; select relationshipsbetween the motion of image features and the previously determinedsubject motion values for a classification method of the plurality ofdifferent classification methods based on the extracted image feature;and determine a subject motion value based on the motion information forthe image feature and the obtained motion classification model.
 12. Thesystem of claim 11, wherein the interface is further configured toobtain the motion classification model based on historical data relatingto the previously determined subject motion values for image featuresextracted from medical slice images.
 13. The system of claim 11, whereinthe at least one processor is further configured to extract the imagefeature by separating the medical slice image into a foreground and abackground, and extracting at least one of a foreground image featurefrom the foreground or a background image feature from the background asthe extracted image feature.
 14. The system of claim 13, whereinextracting the foreground image feature comprises: identifying an imagefeature from at least one of: a spatial domain representation of theforeground, a wavelet domain representation of the foreground, or aspectral domain representation of the foreground; and extracting theidentified image feature as the foreground image feature.
 15. The systemof claim 13, wherein extracting the background image feature comprises:applying radon transformations to the background to generate a pluralityof radon transform profiles; identifying an image feature based on theradon transform profiles; and extracting the identified image feature asthe background image feature.
 16. The system of claim 11, wherein the atleast one processor is further configured to identify a medical sliceimage in which motion of the extracted image feature exceeds a thresholdvalue based on the motion information for the image feature.
 17. Thesystem of claim 11, wherein the at least one processor is furtherconfigured to indicate when the subject has moved excessively during themedical imaging based on the subject motion value.
 18. The method ofclaim 1, further comprising: indicating when the subject has movedexcessively during the medical imaging based on the subject motionvalue.
 19. The method of claim 7, wherein obtaining the motionclassification model comprises: generating the motion classificationmodel based on historical data relating to the previously determinedsubject motion values for image features extracted from medical sliceimages.
 20. The method of claim 7, further comprising: based on themotion information for the image feature, identifying a medical sliceimage in which motion of the extracted image feature exceeds a thresholdvalue.